No Data Scientist Yet? No Problem

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By now I’m sure you’ve heard the term “data scientist.” In fact, it’s being touted as the “sexiest job” of the 21st century by Harvard Business Review. Folks left and right are claiming that data scientists are becoming an integral part of businesses. Even colleges and universities are adding more courses on the topic. While I agree that the data scientist role is and always will be highly sought after, I also believe that there are steps companies can take now to arm their existing employees with the skills required for a data scientist position without actually hiring one.

It’s important to understand why the data scientist role is gaining popularity and traction in the IT world. The primary goal behind big data and analytics is to mine mountains of data and find hidden patterns and insights. Specifically, we’re always looking for enough historical data to assemble models in order to make predictions about the future based on the past. The more data that comes in, the trickier it is for us to assemble these models accurately and in a timely manner. We have to rely on technology to do it the right way and at a moment’s notice.

For example, say you work for a manufacturing company and one of the machines you frequently use has a leaky valve and breaks. This can be expensive and cause a lot of headaches. However if you’re constantly measuring the metrics of every machine all the time (temperature, vibrations, etc.) and are able to analyze piles of historical data, you can see when it failed and potentially make adjustments so it doesn’t happen as often. Furthermore, mining that data allows you to study all the variables and see what metrics have the most power to predict when a valve might fail again. From there, you can study valves in real time and monitor an independent variable’s change in state that prompted a failure in the past, and then can proactively fix the problem before a repair is needed. Predicting the future with some degree of confidence allows us to become better decision makers, and ultimately, run a more efficient and successful business.

While having a designated data scientist to help build predictive models from this data would be nice, they are in low supply. Businesses are desperate to begin the process of having a data scientist on the payroll, but there’s a lot of high science involved in order to augment every decision they make. So how does a company solve the problem of having a knowledge gap between the programmer level and the business analyst level? What can companies do now to take advantage of predictive analytics with existing technology and skillsets, without a data scientist? Let me offer a few suggestions:

Arm your current programmers and business analysts with superior tooling that makes data scientist duties simple and easy to learn. Doing so can empower current employees to tackle deep analytical problems, saving your IT department time and money.

Upgrade the analytics skills of current employees through continuous learning opportunities. Maybe start with DBAs as the data crunchers who already exist?

Research and find a software company that prepackages the skills of a data scientist into software. Instead of waiting for a data scientist to come along, business owners can package up the data, send it to an analytics software-as-a-service provider and then get a prediction back.

Find a college or university that offers degrees/certifications for data scientists and start making connections with those educators. Even if programs are still in the early stages, making connections now will help secure your company gets easier access to future graduates who studied and are experts as data scientists.

It might be 10 years before the industry sees an abundance of data scientists; businesses can’t afford to wait while the market catches up. Successfully analyzing and predicting trends now is what will keep your business ahead of the competition, so it only makes sense to start exploiting the skillsets from your existing employees or utilize software in the interim.

Do you see a direct and urgent need to hire a data scientist within your organization? Fire away with your thoughts in the comments section below.